Element-Wise Alternating Least Squares Algorithm for Nonnegative Matrix Factorization on One-Hot Encoded Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Matrix factorization is a popular technique used in recommender systems based on collaborative filtering. Given a matrix that represents ratings of items by users, one can obtain latent feature vectors of the users and the items by applying one of the existing matrix factorization algorithms. In this paper, we focus our attention on matrices obtained from categorical ratings using one-hot encoding, and propose an element-wise alternating least squares algorithm to obtain latent feature vectors from such matrices. We next show that the proposed algorithm has the global convergence property in the sense of Zangwill. We also show through experiments using a benchmark dataset that the proposed algorithm is effective for prediction of unknown ratings.

Original languageEnglish
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
PublisherSpringer Science and Business Media Deutschland GmbH
Pages342-350
Number of pages9
ISBN (Print)9783030638221
DOIs
Publication statusPublished - 2020
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: Nov 18 2020Nov 22 2020

Publication series

NameCommunications in Computer and Information Science
Volume1333
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference27th International Conference on Neural Information Processing, ICONIP 2020
CountryThailand
CityBangkok
Period11/18/2011/22/20

Keywords

  • Global convergence
  • Nonnegative matrix factorization
  • One-hot encoding
  • Recommender systems

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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